Future work. Our future work will focus on pursuing three major directions: (i) designing new measures complementary to the proposed generalization gap; (ii) investigating how to create and utilize effective embeddings for imbalanced data; and (iii) expanding our oversampling algorithm to continual learning scenarios. As we have seen that the generalization gap can lead to effective oversampling, we envision creating complementary measures will lead to a better understanding the characteristics of multi-class imbalanced problems. This will allow us to modify the feature extraction process to create unbiased embeddings that will enhance the effects of over-sampling. Combining imbalance-aware representation learning [1]} with oversampling in embedding space will allow for even more robust training of deep neural networks. Finally, we plan to investigate the potential of using our augmentation algorithm in the continual learning framework [2]}, where dynamic changes in the generalization gap could be used to handle concept drift and evolving class imbalance [3]}.
